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jmcs()
.AUCjmcs()
area under the ROC curve (AUC) to assess the prediction performance of joint models.surviftjmcs()
.plot.surviftjmcs()
due to theoretical problem.PEjmcs()
and MAEQjmcs()
.surviftjmcs()
for the competing risk and add summary()
for providing parameter estimates and SE for both sub-models.jmcs()
.Provide support for handling categorical variables in both sub-models.
Provide the anova()
function to compare two fitted joint models.
Add the simulate
argument in the survfitjmcs()
function to obtain the conditional probabilities using the Gauss-Hermite quadrature rule for numerical integration.
Adjust the label position of y axis for clarity purposes when include.y = TRUE
in the plot.survfitjmcs()
function.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.